CN112819820B - Road asphalt repairing and detecting method based on machine vision - Google Patents
Road asphalt repairing and detecting method based on machine vision Download PDFInfo
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Abstract
The invention provides a machine vision-based pavement asphalt repairing and detecting method, which comprises the following steps: inputting a pavement image to be detected, and converting the input pavement image to be detected into a gray image from color; calculating a gray average value of the gray image, taking the ratio of the gray average value to a preset threshold value as a gray correction coefficient, and dividing the gray image by the gray correction coefficient to obtain a corrected image; judging the repair type; detecting candidate areas; detecting and describing block repairs; detecting and describing strip-shaped repair; and adding the block repair detection result graph and the strip repair detection result graph, and outputting to obtain a final repair detection result. The method does not need to train and mark the data set, saves the early training cost, can immediately obtain the output picture, accelerates the processing speed and improves the efficiency of the pavement detection system.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a road asphalt repairing and detecting method based on machine vision.
Background
The detection method for pavement asphalt patching mainly comprises a detection method based on a convolutional neural network, a detection method based on a local texture binary pattern and a detection method based on window contrast image feature extraction.
The main thought of each method is as follows:
(1) The road surface repairing detection method based on the convolutional neural network comprises the steps of obtaining a plurality of frames of road surface sample images, and training a plurality of convolutional neural network models to obtain trained network models; determining a target convolutional neural network model of the plurality of trained convolutional neural network models; and inputting the road surface image to be detected into the target convolutional neural network model to obtain road surface repair information in the road surface image to be detected.
(2) The road surface repairing detection method based on the local texture binary pattern is to extract LRBP (local rectangular binary pattern) characteristic vectors of road surface images by adopting a local rectangular binary pattern calculation method aiming at the linear characteristics of road surface repairing diseases, obtain the LRBP characteristic vectors of the road surface repairing diseases and the LRBP characteristic vectors of normal road surface images, obtain a classifier for repairing diseases by utilizing machine learning, and detect and identify the road surface repairing diseases so as to realize the identification and detection of the road surface repairing diseases.
(3) The road surface repairing detection method based on the window contrast is characterized in that firstly, a unified restoration method is used for carrying out picture deblurring on road disease images, a window contrast algorithm is used for extracting repairing image information, then, a window contrast algorithm is used for removing pseudo repairing information by combining original image information for the second time, and image road surface repairing target information is accurately and rapidly extracted.
The existing detection algorithm has the following problems:
(1) The road surface repairing detection method based on the convolutional neural network has the following problems: the real-time detection cannot be achieved, the method is suitable for pictures with high contrast, low noise and simple scenes, does not contain obstacles such as fallen leaves, water stains, lane lines, shadows and the like, underestimates the complexity of road surface repair images, and is difficult to meet the requirements of practical engineering application.
(2) The road surface repairing detection method based on the local texture binary pattern has the following problems: the method is easy to be interfered by background noise during detection and repair, and is suitable for detection in a simple scene with smooth road surface and obvious repair edge. In reality, the road surface condition is complex, the repair shape is complex and changeable, the interference on the extracted feature vector is large, and the detection accuracy faces a great challenge.
(3) The road surface repair detection method based on window contrast has the following problems: the repair area detected by the method is incomplete, a large part of defects exist, the removed pseudo repair area only comprises isolated small target areas such as stains, asphalt or repair raw materials, and the like, and the pseudo repair area which is very similar to repair such as ruts, water stains and the like cannot be removed.
Reference document: zhang Xiuhua, jing Genjiang, wang Ping, on-vehicle automatic detection method for road surface disease repair image [ J ]. Computer and digital engineering, 2014,42 (05): 868-871.
Disclosure of Invention
According to the technical problem that the repairing area is incomplete and has a large part of defects, the pavement asphalt repairing detection method based on machine vision is provided. The invention mainly utilizes a road asphalt repairing and detecting method based on machine vision, which is characterized by comprising the following steps:
step S1: inputting a pavement image to be detected, and converting the input pavement image to be detected from color into a gray image P 1 ；
Step S2: calculating the gray image P 1 Is μ, the ratio of the gray average μ to a preset threshold is used as a gray correction coefficient, and the gray image P 1 Dividing the corrected image by the gray correction coefficient to obtain a corrected image P 2 ；
Step S3: judging the repair type; calculating the gray image P 1 The number of pixels with the middle gray value lower than 0.7 mu is c, and if c is larger than a set threshold value xi, block repair can exist in the image; if c is less than or equal to xi, strip-shaped repair can exist in the image;
step S4: detecting candidate areas; detecting an image P by a region detection method 2 Each of the communication areas in (a); and counting the area of each connected region and displaying the gray level image P 1 A gray average value of (a);
step S5: detecting and describing block repairs;
step S6: detecting and describing strip-shaped repair;
step S7: and adding the block repair detection result graph C and the strip repair detection result graph D, and outputting to obtain a final repair detection result.
Further, the detecting and describing the block repair further comprises the following steps:
step S51: screening the blocky repair areas based on the area attributes; judging whether the area of the current area is in the range [ a ] 1 ,a 2 ]The method comprises the steps of carrying out a first treatment on the surface of the If the area is within the range, judging the area as a blocky repair area from the aspect of area attribute characteristics;
step S52: screening the block repair areas based on gray attribute characteristics; judging the current area in the gray image P 1 If the gray average value in the current region is smaller than the set threshold value d, judging that the current region is a block-shaped repair region from the gray attribute characteristic angle;
step S53: screening a block repair area based on geometric structure characteristics; creating a minimum circumscribed rectangle of the current region, setting the ratio of the number of pixel points of the region in the minimum circumscribed rectangle as q, and judging the current region as a block-shaped repair region from the aspect of the geometric structure characteristic if q is more than delta;
step S54: block repair area description; defining a blank image A with the same size as the input pavement image to be detected, storing all the block repair areas into the image A, marking the block repair areas corresponding to the A in the input pavement image to be detected with yellow color, and generating a block repair detection result graph C.
Further, the detecting and describing the strip repair further comprises the following steps:
step S61: screening strip-shaped repair areas based on area attributes; judging whether the area of the current area is in the range [ a ] 3 ,a 4 ]Inside; if the area is within the range, the area is judged to be a strip-shaped repair area from the aspect of the area attribute characteristics.
Step S62: screening strip-shaped repair areas based on gray attribute characteristics;
step S63: removing water stain interference based on quantity priori; the number of strip-shaped repairs in the pavement image is limited, and tau strips are not exceeded under the general condition; counting the total number of repair areas in the strip repair candidate graph B as n, if n is more than tau, judging that the result is a water stain false detection graph, and emptying the candidate graph B without subsequent screening; if n is less than or equal to tau, carrying out the next step of geometric structural feature screening;
step S64: screening strip-shaped repair areas based on geometric structure characteristics; traversing the communication area, calculating horizontal projection and vertical projection of the area, and removing noise influence by median filtering; setting the average minimum width value of the strip repair as phi, dividing the filtered horizontal and vertical projection sequences by phi respectively, and marking the result sequence obtained by rounding as s 1 、s 2 The method comprises the steps of carrying out a first treatment on the surface of the In the horizontal direction of the resulting sequence s 1 For example, if the total length of the sequence is counted as l, the mode of the sequence is gamma, the maximum length value of the gamma continuous occurrence is lambda, the duty ratio ρ of the maximum length value of the gamma continuous occurrence in the total length of the repair horizontal direction is
If gamma is less than omega and rho is more than theta, judging the area to be a strip-shaped repair area; if the conditions are not met, judging that the connected domain is a false detection water stain region, and removing the region from the candidate diagram B;
step S65: describing a strip-shaped repair area; and marking the strip-shaped repair area corresponding to the image B in the original image to be detected by using yellow color, and generating a final strip-shaped repair detection result image as D.
Further, the screening of the strip-shaped repair area based on the gray attribute features further comprises the following steps:
step S621: setting a gray threshold value: setting the gray level threshold of the detection repair area as alpha and the gray level difference threshold of the repair area and the background area as beta according to the image gray level average value mu;
step S622: screening strip-shaped repairing areas: defining an all-zero image B with the same size as the original image, recording the gray average value of the current area in the original image as eta, judging the current area as a strip-shaped repair area from the gray characteristic angle if eta is smaller than alpha and mu-eta is smaller than beta, and setting the area corresponding to the B of the area as 1; and traversing all the suspected areas, and screening gray features to generate a final strip repair candidate diagram B.
Compared with the prior art, the invention has the following advantages:
the method does not need to train and mark the data set, saves the early training cost, can immediately obtain the output picture, accelerates the processing speed and improves the efficiency of the pavement detection system. There is good performance on different data sets, not only optimized for a single data set.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort to a person skilled in the art.
FIG. 1 is a schematic overall flow chart of the present invention.
FIG. 2 is a diagram showing the input and output images of the strip asphalt patch detection of the present invention; wherein (a) is an input image; (b) outputting the detection result image.
FIG. 3 is a diagram showing the input and output images of the strip asphalt patch detection of the present invention; wherein (a) is an input image; (b) outputting the detection result image.
FIG. 4 is a block asphalt repair detection input/output image of the present invention; wherein (a) is an input image; (b) outputting the detection result image.
FIG. 5 is a block asphalt repair detection input/output image of the present invention; wherein (a) is an input image; (b) outputting the detection result image.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in fig. 1-5, the invention provides a road asphalt repairing and detecting method based on machine vision, which comprises the following steps:
step S1: inputting a pavement image to be detected, and converting the input pavement image to be detected from color into a gray image P 1 ；
Step S2: calculating the gray image P 1 Is μ, the ratio of the gray average μ to a preset threshold is used as a gray correction coefficient, and the gray image P 1 Dividing the corrected image by the gray correction coefficient to obtain a corrected image P 2 ；
Step S3: judging the repair type; calculating the gray image P 1 The number of pixels with the middle gray value lower than 0.7 mu is c, and if c is larger than a set threshold value xi, block repair can exist in the image; if c is less than or equal to xi, strip-shaped repair can exist in the image; in this application, ζ is preferably 0.016 as a preferred embodiment.
Step S4: detecting candidate areas; detecting an image P by a region detection method 2 Each of the communication areas in (a); and counting the area of each connected region and displaying the gray level image P 1 A gray average value of (a);
step S5: the block repair is detected and described. The detection and description of the block repair further comprises the following steps:
step S51: screening the blocky repair areas based on the area attributes; judging whether the area of the current area is in the range [ a ] 1 ,a 2 ]The method comprises the steps of carrying out a first treatment on the surface of the If the area is within the range, the area is judged to be a blocky repair area from the aspect of the area attribute characteristics. In the present application, the above-mentioned range is set to [100000, 350000 ]]It is understood that in other embodiments, the specific range may be set according to the actual area size.
Step S52: screening the block repair areas based on gray attribute characteristics; judging the current area in the gray image P 1 If the gray average value in the current region is smaller than a set threshold value d, wherein d is preferably 90, judging that the current region is a block-shaped repair region from the gray attribute characteristic angle;
step S53: screening a block repair area based on geometric structure characteristics; creating a minimum circumscribed rectangle of the current region, setting the ratio of the number of pixel points of the region in the minimum circumscribed rectangle as q, and judging the current region as a block-shaped repair region from the aspect of the geometric structure characteristic if q is more than delta, wherein delta is preferably 0.7;
step S54: block repair area description; defining a blank image A with the same size as the input pavement image to be detected, storing all the block repair areas into the image A, marking the block repair areas corresponding to the A in the input pavement image to be detected with yellow color, and generating a block repair detection result graph C.
Step S6: the strip repair is detected and described. The detecting and describing the strip repair further comprises the following steps:
step S61: screening strip-shaped repair areas based on area attributes; judging whether the area of the current area is in the range [ a ] 3 ,a 4 ]Within, a preferred range of [3000,80000 ]]The method comprises the steps of carrying out a first treatment on the surface of the If the area is within the range, the area is judged to be a strip-shaped repair area from the aspect of the area attribute characteristics.
Step S62: and (5) screening the strip-shaped repair area based on the gray attribute characteristics.
The strip-shaped repair area screening based on the gray attribute features further comprises the following steps:
step S621: setting a gray threshold value: setting the gray level threshold of the detection repair area as alpha and the gray level difference threshold of the repair area and the background area as beta according to the image gray level average value mu;
step S622: screening strip-shaped repairing areas: defining an all-zero image B with the same size as the original image, recording the gray average value of the current area in the original image as eta, judging the current area as a strip-shaped repair area from the gray characteristic angle if eta is smaller than alpha and mu-eta is smaller than beta, and setting the area corresponding to the B of the area as 1; and traversing all the suspected areas, and screening gray features to generate a final strip repair candidate diagram B.
Step S63: removing water stain interference based on quantity priori; the number of strip repairs in the pavement image is limited, and is generally not more than tau, wherein tau is preferably 3; counting the total number of repair areas in the strip repair candidate graph B as n, if n is more than tau, judging that the result is a water stain false detection graph, and emptying the candidate graph B without subsequent screening; if n is less than or equal to tau, carrying out the next step of geometric structural feature screening;
step S64: screening strip-shaped repair areas based on geometric structure characteristics; traversing the communication area, calculating horizontal projection and vertical projection of the area, and removing noise influence by median filtering; setting the average minimum width value of the strip repair as phi, dividing the filtered horizontal and vertical projection sequences by phi respectively, and marking the result sequence obtained by rounding as s 1 、s 2 The method comprises the steps of carrying out a first treatment on the surface of the In the horizontal direction of the resulting sequence s 1 For example, if the total length of the sequence is counted as l, the mode of the sequence is gamma, the maximum length value of the gamma continuous occurrence is lambda, the duty ratio ρ of the maximum length value of the gamma continuous occurrence in the total length of the repair horizontal direction is
If gamma is less than omega and rho is more than theta, wherein omega is preferably 3, and theta is preferably 0.7, judging the area as a strip-shaped repair areaA domain; if the conditions are not met, judging that the connected domain is a false detection water stain region, and removing the region from the candidate diagram B;
step S65: describing a strip-shaped repair area; and marking the strip-shaped repair area corresponding to the image B in the original image to be detected by using yellow color, and generating a final strip-shaped repair detection result image as D.
Step S7: and adding the block repair detection result graph C and the strip repair detection result graph D, and outputting to obtain a final repair detection result.
Examples:
1) The experimental results are shown in fig. 2 and 3, the strip asphalt repair area detected by using the gray marks can be completely and accurately detected and described in the road surface image, and the method has good detection effect on the road surface repair images with a large number and complex shapes.
2) As shown in fig. 4 and 5, the block asphalt repair area detected by the gray mark can be completely and accurately detected and described in the road surface image.
3) According to the method, training and labeling of the data set are not needed, the early training cost is saved, the output picture can be obtained immediately, the processing speed is increased, and the efficiency of the pavement detection system is improved. There is good performance on different data sets, not only optimized for a single data set.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments. In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (4)
1. The machine vision-based pavement asphalt repairing and detecting method is characterized by comprising the following steps of:
s1: inputting a pavement image to be detected, and converting the input pavement image to be detected from color into a gray image P 1 ；
S2: calculating the gray image P 1 Is μ, the ratio of the gray average μ to a preset threshold is used as a gray correction coefficient, and the gray image P 1 Dividing the corrected image by the gray correction coefficient to obtain a corrected image P 2 ；
S3: judging the repair type; calculating the gray image P 1 The number of pixels with the middle gray value lower than 0.7 mu is c, and if c is larger than a set threshold value xi, block repair can exist in the image; if c is less than or equal to xi, strip-shaped repair can exist in the image;
s4: detecting candidate areas; detecting an image P by a region detection method 2 Each of the communication areas in (a); and counting the area of each connected region and displaying the gray level image P 1 A gray average value of (a);
s5: detecting and describing block repairs;
s6: detecting and describing strip-shaped repair;
s7: and adding the block repair detection result graph C and the strip repair detection result graph D, and outputting to obtain a final repair detection result.
2. The machine vision-based pavement asphalt repair detection method according to claim 1, wherein the block repair detection and description further comprises the steps of:
s51: area attribute-based pairing blocksScreening the shape repairing area; judging whether the area of the current area is in the range [ a ] 1 ,a 2 ]The method comprises the steps of carrying out a first treatment on the surface of the If the area is within the range, judging the area as a blocky repair area from the aspect of area attribute characteristics;
s52: screening the block repair areas based on gray attribute characteristics; judging the current area in the gray image P 1 If the gray average value in the current region is smaller than the set threshold value d, judging that the current region is a block-shaped repair region from the gray attribute characteristic angle;
s53: screening a block repair area based on geometric structure characteristics; creating a minimum circumscribed rectangle of the current region, setting the ratio of the number of pixel points of the region in the minimum circumscribed rectangle as q, and judging the current region as a block-shaped repair region from the aspect of the geometric structure characteristic if q is more than delta;
s54: block repair area description; defining a blank image A with the same size as the input pavement image to be detected, storing all the block repair areas into the image A, marking the block repair areas corresponding to the A in the input pavement image to be detected with yellow color, and generating a block repair detection result graph C.
3. The machine vision-based pavement asphalt repair detection method according to claim 1, wherein the detecting and describing the strip repair further comprises the steps of:
s61: screening strip-shaped repair areas based on area attributes; judging whether the area of the current area is in the range [ a ] 3 ,a 4 ]Inside; if the area is within the range, judging the area as a strip-shaped repair area from the aspect of the area attribute characteristics;
s62: screening strip-shaped repair areas based on gray attribute characteristics;
s63: removing water stain interference based on quantity priori; the number of strip-shaped repairs in the pavement image is limited, and tau strips are not exceeded under the general condition; counting the total number of repair areas in the strip repair candidate graph B as n, if n is more than tau, judging that the result is a water stain false detection graph, and emptying the candidate graph B without subsequent screening; if n is less than or equal to tau, carrying out the next step of geometric structural feature screening;
s64: screening strip-shaped repair areas based on geometric structure characteristics; traversing the communication area, calculating horizontal projection and vertical projection of the area, and removing noise influence by median filtering; setting the average minimum width value of the strip repair as phi, dividing the filtered horizontal and vertical projection sequences by phi respectively, and marking the result sequence obtained by rounding as s 1 、s 2 The method comprises the steps of carrying out a first treatment on the surface of the In the horizontal direction of the resulting sequence s 1 For example, if the total length of the sequence is counted as l, the mode of the sequence is gamma, the maximum length value of the gamma continuous occurrence is lambda, the duty ratio ρ of the maximum length value of the gamma continuous occurrence in the total length of the repair horizontal direction is
If gamma is less than omega and rho is more than theta, judging the area to be a strip-shaped repair area; if the conditions are not met, judging that the connected domain is a false detection water stain region, and removing the region from the candidate diagram B;
s65: describing a strip-shaped repair area; and marking the strip-shaped repair area corresponding to the image B in the original image to be detected by using yellow color, and generating a final strip-shaped repair detection result image as D.
4. The machine vision-based pavement asphalt repair detection method according to claim 3, wherein the screening of the strip repair area based on the gray attribute features further comprises the steps of:
s621: setting a gray threshold value: setting the gray level threshold of the detection repair area as alpha and the gray level difference threshold of the repair area and the background area as beta according to the image gray level average value mu;
s622: screening strip-shaped repairing areas: defining an all-zero image B with the same size as the original image, recording the gray average value of the current area in the original image as eta, judging the current area as a strip-shaped repair area from the gray characteristic angle if eta is smaller than alpha and mu-eta is smaller than beta, and setting the area corresponding to the B of the area as 1; and traversing all the suspected areas, and screening gray features to generate a final strip repair candidate diagram B.
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